{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "f89242ce",
      "metadata": {},
      "source": [
        "# Gal3 Autonomous Surface Vessel IMU EKF\n",
        "\n",
        "This notebook rewrites parts of Gal3ImuExample to make it work with the data found in gazebo_data.csv\n",
        "\n",
        "\n",
        "This tutorial shows how to use GTSAM’s Gal3-specific EKF (Gal3ImuEKF) with synthetic IMU data and occasional position measurements. You’ll learn:\n",
        "- How the predict step integrates IMU into a Gal3 increment and propagates covariance.\n",
        "- How to write a position measurement in the EKF’s local coordinates (the correct H = [0, 0, R, 0]).\n",
        "- How to interpret the results with ±2σ uncertainty bands for yaw/pitch/roll, velocity, and position.\n",
        "\n",
        "We simulate a simple motion and run the EKF end-to-end, then visualize estimates vs. ground truth along with uncertainty.\n",
        "\n",
        "Authors: Derek Benham & Frank Dellaert"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "license_cell",
      "metadata": {
        "tags": [
          "remove-cell"
        ]
      },
      "source": [
        "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\nAtlanta, Georgia 30332-0415\nAll Rights Reserved\n\nAuthors: Frank Dellaert, et al. (see THANKS for the full author list)\n\nSee LICENSE for the license information"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f4724d5a",
      "metadata": {},
      "source": [
        "<a href=\"https://colab.research.google.com/github/borglab/gtsam/blob/develop/python/gtsam/examples/Gal3ImuASVExample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "e1b272ef",
      "metadata": {
        "tags": [
          "remove-cell"
        ]
      },
      "outputs": [],
      "source": [
        "# Install GTSAM from pip if running in Google Colab\n",
        "try:\n",
        "    import google.colab  # type: ignore\n",
        "    %pip install --quiet gtsam-develop\n",
        "except Exception:\n",
        "    pass  # Not in Colab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "fdd02b60",
      "metadata": {
        "tags": [
          "hide-input"
        ]
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "\n",
        "import gtsam\n",
        "from gtsam import Gal3, Point3, Pose3, Rot3\n",
        "\n",
        "\n",
        "# Helper to display yaw/pitch/roll in degrees\n",
        "def rot3_ypr_rad(R: Rot3):\n",
        "    \"\"\"Return yaw, pitch, roll (degrees) for a Rot3.\"\"\"\n",
        "    return np.degrees(R.ypr())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "41612211",
      "metadata": {},
      "source": [
        "## Background and setup\n",
        "\n",
        "State: Gal3 X = (R, v, p, t) with rotation R ∈ SO(3), velocity v ∈ R^3, position p ∈ R^3, time t ∈ R.\n",
        "- EKF local coordinates are [δθ, δv_body, δp_body, δt], i.e., position/velocity increments are expressed in the body frame.\n",
        "- For a world-position measurement z ≈ p_world, the linearized measurement Jacobian is H = [0, 0, R, 0].\n",
        "\n",
        "We’ll now define a simple scenario that provides ground truth and noise-free IMU to drive the predict step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "88e15df8",
      "metadata": {
        "tags": [
          "hide-input"
        ]
      },
      "outputs": [],
      "source": [
        "# Single function to convert CSV into as similar as input used in NavStateImuExample\n",
        "def parse_data(filename):\n",
        "    # read csv file as np\n",
        "    data_np = np.genfromtxt(filename, delimiter=\",\", dtype=None, skip_header=1)  # type: ignore\n",
        "    start = data_np[1, 7:]\n",
        "    imu_meas = [\n",
        "        (row[1:4], row[4:7]) for row in data_np[1:]\n",
        "    ]  # skipping first row because of gt initialization\n",
        "    data_gt = data_np[:, 7:]\n",
        "\n",
        "    dts = np.diff(\n",
        "        data_np[:, 0]\n",
        "    )  # I found that not all dt values are the same, therefore I'm using an array.\n",
        "    position = start[:3]\n",
        "    quat = start[3:7]  # x,y,z,w\n",
        "    velocity_body = start[7:10]\n",
        "\n",
        "    rot = Rot3.Quaternion(quat[3], quat[0], quat[1], quat[2])\n",
        "\n",
        "    pose = Pose3(rot, Point3(*position))\n",
        "    velocity_world = rot.rotate(velocity_body)\n",
        "    X0 = Gal3(rot, pose.translation(), velocity_world, 0.0)\n",
        "\n",
        "    pos_true = data_gt[:, 0:3]\n",
        "    quat_true = data_gt[:, 3:7]\n",
        "    vel_true_body = data_gt[:, 7:10]\n",
        "    vel_true_world = np.empty_like(vel_true_body)\n",
        "    ypr_true = np.empty((vel_true_body.shape[0], 3))\n",
        "    for i in range(vel_true_body.shape[0]):\n",
        "        rot = Rot3.Quaternion(\n",
        "            quat_true[i, 3], quat_true[i, 0], quat_true[i, 1], quat_true[i, 2]\n",
        "        )\n",
        "        vel_true_world[i] = rot.rotate(vel_true_body[i])\n",
        "        ypr_true[i] = rot3_ypr_rad(rot)\n",
        "\n",
        "    return X0, imu_meas, pos_true, ypr_true, vel_true_world, dts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "6a6557a9",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of IMU measurements: 988\n",
            "Number of time steps (N): 988\n",
            "Shape of position ground truth: (989, 3)\n",
            "Shape of velocity ground truth: (989, 3)\n",
            "Shape of yaw/pitch/roll ground truth: (989, 3)\n",
            "Min/Max delta t: 0.01000 -> 0.03000\n",
            "Initial Gal3: ypr (deg) = [-176.68, -0.56, 0.14], vel = [-2.04, -0.14, 0.26] pos = [220.43, -1.49, 0.03], time = 0.00]\n"
          ]
        }
      ],
      "source": [
        "filename = gtsam.findExampleDataFile(\"gazebo_ASV.csv\")\n",
        "if filename is None:\n",
        "    raise RuntimeError(\n",
        "        \"Could not find example data file 'gazebo_ASV.csv'. Make sure GTSAM is installed correctly.\"\n",
        "    )\n",
        "\n",
        "X0, imu_meas, pos_true, ypr_true, vel_true, dts = parse_data(filename)\n",
        "N = dts.shape[0]\n",
        "\n",
        "print(f\"Number of IMU measurements: {len(imu_meas)}\")\n",
        "print(f\"Number of time steps (N): {N}\")\n",
        "print(f\"Shape of position ground truth: {pos_true.shape}\")\n",
        "print(f\"Shape of velocity ground truth: {vel_true.shape}\")\n",
        "print(f\"Shape of yaw/pitch/roll ground truth: {ypr_true.shape}\")\n",
        "print(f\"Min/Max delta t: {np.min(dts):.5f} -> {np.max(dts):.5f}\")\n",
        "ypr_deg = np.round(np.degrees(X0.attitude().ypr()), 2)\n",
        "pos = np.round(X0.position(), 2)\n",
        "vel = np.round(X0.velocity(), 2)\n",
        "print(\n",
        "    f\"Initial Gal3: ypr (deg) = [{ypr_deg[0]:.2f}, {ypr_deg[1]:.2f}, {ypr_deg[2]:.2f}], \"\n",
        "    f\"vel = [{vel[0]:.2f}, {vel[1]:.2f}, {vel[2]:.2f}] \"\n",
        "    f\"pos = [{pos[0]:.2f}, {pos[1]:.2f}, {pos[2]:.2f}], \"\n",
        "    f\"time = {X0.time():.2f}]\",\n",
        "    flush=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "05911a10",
      "metadata": {},
      "source": [
        "## Simulation issues\n",
        "It appears that my simulation data is not perfect and that there are some dropped messages. See the chart below for all occurrences of time steps that are greater than 0.01"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "735490dc",
      "metadata": {
        "tags": [
          "hide-input"
        ]
      },
      "outputs": [
        {
          "data": {
            "application/vnd.plotly.v1+json": {
              "config": {
                "plotlyServerURL": "https://plot.ly"
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              "data": [
                {
                  "line": {
                    "color": "blue"
                  },
                  "mode": "lines",
                  "name": "Delta (s)",
                  "type": "scatter",
                  "y": {
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                  "title": {
                    "text": "Delta (s)"
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          "text": [
            "Indices of deltas greater than 0.01: [221 391 512 513 514 648]\n",
            "The maximum delta between consecutive timestamps is: 0.03000000000000025\n"
          ]
        }
      ],
      "source": [
        "# Find the maximum delta\n",
        "max_delta = np.max(dts)\n",
        "\n",
        "# Create a Plotly figure\n",
        "fig = go.Figure()\n",
        "\n",
        "# Add a line plot for deltas\n",
        "fig.add_trace(\n",
        "    go.Scatter(y=dts, mode=\"lines\", name=\"Delta (s)\", line=dict(color=\"blue\"))\n",
        ")\n",
        "\n",
        "# Update layout for the plot\n",
        "fig.update_layout(\n",
        "    title=\"Delta between Consecutive Timestamps\",\n",
        "    xaxis_title=\"Index\",\n",
        "    yaxis_title=\"Delta (s)\",\n",
        "    template=\"plotly_white\",\n",
        ")\n",
        "\n",
        "# Show the plot\n",
        "fig.show()\n",
        "\n",
        "# Find the indices where the delta is greater than 0.01\n",
        "large_deltas_idx = np.where(dts > 0.010001)[0]  # In some cases dt is 0.0100000000001\n",
        "print(f\"Indices of deltas greater than 0.01: {large_deltas_idx}\")\n",
        "\n",
        "print(f\"The maximum delta between consecutive timestamps is: {max_delta}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "22a1d818",
      "metadata": {},
      "source": [
        "## Create Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "9c6085bd",
      "metadata": {},
      "outputs": [],
      "source": [
        "params = gtsam.PreintegrationParams.MakeSharedU(9.81)  # gravity in m/s^2\n",
        "\n",
        "# # IMU covariances (tune as needed)\n",
        "params.setAccelerometerCovariance(np.diag([1e-3] * 3))  # type: ignore\n",
        "params.setIntegrationCovariance(np.diag([1e-3] * 3))  #  type: ignore\n",
        "params.setGyroscopeCovariance(np.diag([1e-4] * 3))  # type: ignore"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7a9fab50",
      "metadata": {},
      "source": [
        "## Run the EKF\n",
        "\n",
        "We integrate IMU in the predict step and occasionally update with a position measurement. The position Jacobian must be H = [0, R, 0] because EKF local coordinates use body-frame increments for p and v."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "7698e840",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Initialize EKF with ground-truth state and modest covariance\n",
        "P0 = np.eye(10) * 0.1  # std ~ 0.316 on each component\n",
        "P0[9, 9] = 1e-9  # tiny variance for time\n",
        "ekf = gtsam.Gal3ImuEKF(X0, P0, params)  # type: ignore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "98ea7672",
      "metadata": {},
      "outputs": [],
      "source": [
        "times = np.concatenate(([0.0], np.cumsum(dts)))\n",
        "\n",
        "# Storage for plotting\n",
        "ypr_est = []\n",
        "vel_est = []\n",
        "pos_est = []\n",
        "time_est = []\n",
        "\n",
        "# Std-dev storage from EKF covariance (for ±2σ bands)\n",
        "rot_std_deg_list = []  # yaw/pitch/roll std in degrees\n",
        "pos_std_list = []  # x/y/z std in meters\n",
        "vel_std_list = []  # vx/vy/vz std in m/s\n",
        "time_std_list = []  # time std in seconds\n",
        "\n",
        "# Simulate and filter\n",
        "Xk = X0\n",
        "for k, t in enumerate(times):\n",
        "    if k == 0:\n",
        "        # Record initial estimate and covariance\n",
        "        X_est = ekf.state()\n",
        "        ypr_est.append(rot3_ypr_rad(X_est.attitude()))\n",
        "        vel_est.append(np.asarray(X_est.velocity()))\n",
        "        pos_est.append(np.asarray(X_est.position()))\n",
        "        time_est.append(X_est.time())\n",
        "        P = ekf.covariance()\n",
        "        std = np.sqrt(np.diag(P))\n",
        "        rot_std_deg_list.append(np.rad2deg(std[0:3]))\n",
        "        vel_std_list.append(std[3:6])\n",
        "        pos_std_list.append(std[6:9])\n",
        "        time_std_list.append(std[9])\n",
        "        continue\n",
        "\n",
        "    # Predict using noise-free IMU from scenario (at interval start)\n",
        "    omega_meas = np.array(imu_meas[k-1][0])\n",
        "    acc_meas = np.array(imu_meas[k-1][1])\n",
        "    dt = dts[k-1]\n",
        "    ekf.predict(omega_meas, acc_meas, dt)\n",
        "\n",
        "    # Log estimate after predict\n",
        "    X_est = ekf.state()\n",
        "    ypr_est.append(rot3_ypr_rad(X_est.attitude()))\n",
        "    vel_est.append(np.asarray(X_est.velocity()))\n",
        "    pos_est.append(np.asarray(X_est.position()))\n",
        "    time_est.append(X_est.time())\n",
        "\n",
        "    # Log current covariance std-devs\n",
        "    P = ekf.covariance()  # post-predict, pre-update\n",
        "    std = np.sqrt(np.diag(P))\n",
        "    rot_std_deg_list.append(np.rad2deg(std[0:3]))\n",
        "    vel_std_list.append(std[3:6])\n",
        "    pos_std_list.append(std[6:9])\n",
        "    time_std_list.append(std[9])\n",
        "\n",
        "    # Position measurement every M steps\n",
        "    M = 100  # measurement cadence\n",
        "    if k % M == 0 and k > 0:\n",
        "        predicted_position = X_est.position()\n",
        "        measured_position = pos_true[k]\n",
        "        R = np.eye(3) * 1.0  # measurement covariance (tune as needed)\n",
        "\n",
        "        # Measurement Jacobian in EKF local coords: H = [0, R, 0]\n",
        "        H = np.zeros((3, 10))\n",
        "        H[:, 6:9] = X_est.attitude().matrix()\n",
        "        ekf.updateWithVector(predicted_position, H, measured_position, R)  # type: ignore\n",
        "\n",
        "# Convert to arrays for plotting\n",
        "ypr_est = np.unwrap(np.vstack(ypr_est), axis=0)\n",
        "vel_est = np.vstack(vel_est)\n",
        "pos_est = np.vstack(pos_est)\n",
        "time_est = np.array(time_est)\n",
        "\n",
        "rot_std_deg = np.vstack(rot_std_deg_list)\n",
        "pos_std = np.vstack(pos_std_list)\n",
        "vel_std = np.vstack(vel_std_list)\n",
        "time_std = np.array(time_std_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "844223ec",
      "metadata": {},
      "source": [
        "## Plot Yaw/Pitch/Roll (degrees)\n",
        "\n",
        "We show estimated angles with ±2σ bands and dashed ground truth. Ideally, the truth lies mostly within the shaded bands if covariances are well tuned."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "b16a8666",
      "metadata": {
        "tags": [
          "hide-input"
        ]
      },
      "outputs": [
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                  "yaxis": "y"
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                  "fillcolor": "rgba(255,0,0,0.15)",
                  "line": {
                    "color": "#ff0000",
                    "width": 0
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                  "showlegend": false,
                  "type": "scatter",
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y"
                },
                {
                  "line": {
                    "color": "#ff0000",
                    "width": 2
                  },
                  "name": "Yaw est",
                  "type": "scatter",
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y"
                },
                {
                  "line": {
                    "color": "#ff0000",
                    "dash": "dash",
                    "width": 2
                  },
                  "name": "Yaw true",
                  "type": "scatter",
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",
                    "dtype": "f8"
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                  "yaxis": "y2"
                },
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                  "fill": "tonexty",
                  "fillcolor": "rgba(0,128,0,0.15)",
                  "line": {
                    "color": "#008000",
                    "width": 0
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                  "showlegend": false,
                  "type": "scatter",
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ABcCrrd54Ew4FwNnN/RYU3QTAPREun+etBMCPeyw2ZoAEwA/LczOeVATAzJYyv8cqBMAR5t5k8gIEwFy+7vv43APAva4Mn/i4A8CY9EKDDpcDwFmKK2sXdwPA3bIoiSRZA8Bd4WJSTj0DwNxhYwJuIwPA6ia4TnoLA8C+qBGou/UCwHO3jxcm4gLApxCZcunPAsDAzZJ1W74CwGh+X/IErQLACJchlxObAsD/XksKAIgCwO/9kl+YcwLAB53Z+b1cAsDmmRiXEUECwDYYzJygIALAk1jcJpj7AcDvlwfocM8BwOLLVYfwmwHAsk/sY1FhAcBccZfwOB8BwAJy5TsF1QDAbLnfWvAM+L8+uSZNSVz3v442HUpqm/a/u7gnyZnK9b+1RVAHg+r0vwf2ggiU+vO/bI3Nrn768r9xH07JzOrxv6bKmRTJy/C/1KyuBrU477+ms+8mhLrsvzAh3Gj2H+q/YKWzSh854b/Y6/O0k+jVv8CskIdQmKO/IGxokCwFsD8wQ+f9Qb/EP3D4QpKGpNA/cF85wEXE1j/4uHKI4q3cPwS5eIwSK+E/ZJBfPYjb4z+Mvio+BmHmPxhExtfUteg/gMXDNFLX6j+8WDkmxMDsP0C6jEonbe4/PN4LpBLZ7z8OTLh2EoHwPwbxTSmq8vA/pGy8jSBA8T+O37/7XGnxP67qvgPBbfE/3rQRyplM8T/OU57jHAbxP0oan9yZmvA/HlTo9wgK8D8UAuxtqqnuP2wydngO+Ow/eM3ej5kB6z/AiFAcc8noPyxliPzyUeY/1BVPx06f4z8U2K+WK7bgP3AhWkFkNNs/SLkcmUKe1D9gki602WzLPyDBzxlsG7o/AEd0a55jjb+AgglVFybBvxAxQEPxY9C/MKxev9JX2L9YyzD1RjLgv1iGWURTPuS/WKYGcUxL6L8UzZ2sAFfsv0bY5aehLfC/nnNGShwp8r/NktckHR70v62zaSU7Cva/Qj/ylePq979RpktYR7/5v4yVAWXXhvu/ZEqc0Hs//b9YQ6rIqef+v/j9YBxvQADAqoxry8wEAcApdav8McABwNioMKUzcwLAxxXkd78dA8Bsk3g/Ub8DwLnucnAKWATAUzixteroBMC+fAgIrnEFwJo5OrwZ8gXAnuMdPuhrBsA2D8YhLN8GwHuiLVXLSwfARNJsHd+yB8DypCpzjBUIwK0hkuPVcwjA5T05XfvNCMA79lgJViYJwCTZoDndfAnAs4K8DnLRCcBK11G85SUKwFBBL+fFegrA/GKLma7PCsC/z38+ASULwD9tAsktfAvAdtV6PbfUC8DcnlRCGi4MwGrEkbshigzAcijqpoToDMALHnxftEgNwGqA9K47qw3AkMv7HiwQDsCM4olmfXYOwHwJ/GPu3Q7AfPbVCBBID8AKKcxxJoEMwDT7g62s7wzASQKo+2JgDcDmU+gxGNMNwIQDHCD4Rg7AMkSoVqC7DsD0rHyMUjEPwE0upM0ipw/AzRwazRgOEMB1PFnuekgQwMu2ew5lghDADAWHN267EMAqBXPTffMQwM0AAmN1KhHA9GxPuQtgEcD29QqQAZQRwHvlJMZpxhHA1JK3qPr2EcC5+PafbiUSwNJFqbCqURLAzIQSxXl7EsBuDsldmKISwDLb36XLxhLApn4yltrnEsCmNK9BjAUTwMpaylWsHxPAK05+jeU1E8BgdQRWAUgTwFFT1SDZVRPA7cIG5itfE8CELVEavmMTwFtGIOWIYxPA+BS/TJNeE8A4WUV+cFQTwFQJrHNIRRPA3mrpGlYxE8CU7dGhVhgTwJyVbvpi+hLAow+HOtzXEsAQAZgK57ASwNsrFg9ThRLAtHOBzpNVEsBZiGHJJSISwNF1Hbaf6hHAmCw9p3OvEcD2Avp7OHERwERAFiD2LxHAmhnMKrjrEMDmqi8d2QoQwOxekeOSfA/A2ObuW2DhDsCFusmTn0MOwESSG2beow3AK6zUA10DDcC44b2zkGIMwGt6HGn1wAvAMq881b0fC8AWoLGwHIAKwCKB1sTV4AnALlo3yKZCCcAXun2TmKYIwEymNpCHDAjAvfU7wC10B8CuCo4Edt4GwD08rxIWTAbAB629VuO7BcC6KEaFsy4FwCBX6nlepQTAYoNx7DMfBMBjoRbNU5wDwChoZyl3HQPA78O6JcKiAsBJD3zTkCsCwHmGlSGAuAHAonPGUSxKAcBB5C7JqN8AwLZeV0dbeQDA+E2FQcwXAMBaQ1t6bXX/vzsXdH2bw/6/SBrfkvwa/r/9ka3JF3z9v5L7my5x5fy/Rkyvnq1X/L/prLgnXtP7v3DhJ9G6V/u/XJMp88Tk+r/sHFEmAnv6v/zreoNwGvq/yUV5KWvC+b9Uj6kHTHP5v/QZpLVWLfm/x5HEIbfv+L/PSmL1err4v3C5wKC8jfi/wbj39TFp+L8uNxy7h0z4v8xERT2xN/i//bCrp4Yq+L8ww1CrgiT4vx+TuUiOpPO/NZWdjnWu878X64MjJb/zv4Pn/V5r1vO/rjqnHAb087/XYVXWwxf0v610wm2fQfS/YAbUtzVx9L+axp16F6b0v47Gao154PS/9OCLNPsf9b/dv5HBOWT1vy625uZErfW/g6J7fx/79b+gxVs2SU32vwjK4IFRo/a/fcCIqI/99r8kTK4QZFv3v3jusmkovPe/QlNZ4eof+L/iFPLiSob4v3aW5PuY7vi/Gnor0W1Y+b+KOm1uusP5v2bfE4+yL/q/ZGXepISb+r+EGnJRMwf7vxBOWboKcvu/pr5MtFzb+7+T9xyc30L8v7gtJhJmqPy/NmwUJ2QL/b+7D7hHVGv9vzJf8x1NyP2/ZNjQ6d8h/r98CNY2p3f+v7hNgwORyf6/bqPnJlYX/78VI+GEn2D/vxTcJzpKpf+/dxbm6l/l/78cXz+PWhAAwKxWjJ6TKwDAf81WvFVEAMBmGTnNrVoAwBIc7YambgDAYRrg90SAAMAkC4MUk48AwJ93yAi1nADAWgWu0senAMAOEm0307AAwFGzJFoLuADA3py1QKG9AMAosl8DpsEAwMY62iRYxADAC5UFvQDGAMBu2WYwwsYAwNcohQamxgDAwl9MmeTFAMCuTSFursQAwHDbDbi2wgDAaQa9BAPAAMD/Hi0svrwAwGZ/cxfNuADA3tsNLgW0AMBilde8iq4AwM/1QKR3qADA+Omds3OhAMCIkTI0opkAwKg5EjomkQDAh6We6b6HAMAiQk7jZH0AwCQZZEk4cgDADQgoUC5mAMC/pIrx/lgAwEIKjq/OSgDAuGHhNsY7AMBItSYWiCsAwGi+6jotGgDAbluQj+IHAMA0xf+6I+n/v4BQU2hOwP+/UA5X86aV/7/+ow7odmn/vxiF32A5O/+/JgFf6UEL/7/uVnv/0dn+vwphfaqVpv6/KFyitZZx/r+S4zvvGjv+v6TuS/wzA/6/qu/uMrXJ/b8cMdhX7479v3K8ofo/U/2/Eqo8p+cW/b84btJ4itr8v0yZ90XPnvy/n2OzEUhk/L9W0KcOgyv8v8AhWTcV9fu/+3sTxjiR979atxNDymH3vyOrQsuXNve/XkRyghAQ978YU4qhze72v1z6ml4/0/a/DIAmZqe99r/kFsSDVq72vzanO8PCpfa/QphJtPGj9r/svkAV0aj2v3ISEUv6tPa/XryYhUrI9r9OLUywheL2v3Gak4zeA/e/Esm6/28s97/utu63wFv3v5adP9l0kfe/jtoSew7O978ESfwE6RD4v7glmm5GWfi/sBK6hVOn+L/C+rJMv/r4v6lHFSPUUvm/bMWoezmv+b+9Xw7uBhD6v12CS6d2dPq/u1HkbMjb+r8QtbeFZ0b7v6KeWWG1s/u/LO8MofUi/L/sEHDhB5T8v/YoaqfQBv2/oUueiKp6/b+HRi7OLe/9vzphNfDWZP6/QkFnug7b/r/2uyUuSVH/vwqXyanLx/+/Ip6XqD0fAMBOM0nyeFoAwBoFnjeOlQDAzOAhbq/QAMDPSAk60QsBwFKCuXjTRgHAkvVJH82BAcAL4sqSqbwBwLF09gQ79wHAnvL6HlAxAsAc+HK84moCwJjlrMnVowLAGbAnbfvbAsCWrLnbJxMDwF4ziwdWSQPAhJIWCnR+A8B6rzoCSrIDwGMYLrby5APA9tgFfJwWBMDNsHLTKUcEwN1ebU2idgTAzvk0pGSlBMApQ/banNMEwMgy8vQvAQXA5yRUrr4uBcCidE7BmlwFwD7dKAeuigXAyQJl3ia5BcAE1KZjLegFwDCixwWdFwbAattqgtFGBsATISQ53nUGwLbE78+3pAbAie5A+L3SBsDot4MR1P8GwBnJli0BLAfA+KjZ7PVWB8BTmoOfKIAHwApsyhGypwfAwbN6aprNB8DG07WGLvEHwAAUmsKDEgjAIplLILwxCMAcKFUblE4IwNrPmUHnaAjAseYyH/WACMDWt8+P4JYIwPliPKVSqgjAOl6VJqC7CMD4YUDgEMsIwLeznvKD2AjA1gX5yiTkCMDIkS4lP+4IwD5Dd07l9gjAHBau+Qb+CMDm6N/+3gMJwGq6cTWQCAnA/cd8DfULCcDk0gCgMw4JwE70iOhhDwnAs+RVKWwPCcDKII/qAe0GwAGktSeB7QbARd6IfxbtBsA0RvqGg+sGwDxLlAOv6AbAJUhB6orkBsAGUQu3094GwCFDiZta1wbA8rJrFRHOBsCjNlO3z8IGwIM8pUU3tQbAS7yrQTylBsBv+Z7G/5IGwOcdwAwOfgbAP12ZRWJmBsDJi+mu+ksGwGJdeluBLgbAfLu8ROANBsD2Hm0Sc+oFwPb9hK98xAXAawSprXqbBcBfsR4V/W8FwEmSU1SsQgXA/tQT6ScTBcDoRkRbzeEEwDbuVq9NrwTAEI/Tw8F7BMAvX/TcoUYEwDUhiBZ5EATA19366MXZA8BKV7FZZ6EDwNcTX2a7ZwPAaJCezkgtA8Cz8Updm/ECwO2iDk8jtALA3tA9dUp1AsBA1LNYQTUCwF0uC0Sc8gHAxYskfrutAcBGk1vkC2cBwNoYVi2gHQHAfMKv6krRAMBKHPEdeoIAwPbDpywaMQDAmYkRy7C4/7+CzjzRYQn/v+LWqoFNVf6/HAS2E2Wa/b/wYBWFX9n8v2x0a8mOE/y/tU1Xl9lI+7/qMIHvEHn6v2RGnyDEpfm/sTLOJ1rQ+L/LiJeQDvj3v6Tq7qaeHve/kJrFE99F9r+qQ9KKSW71v04QE64jmfS/HYzQT2vI87+5ilRh5P3yv4wf5iGKOvK/TATKl0KA8b8n4GUqF9Hwv3CKRZ8sLvC/2C1cZ/Ay77/QCwVq/Snuv2C7XLoQRe2/huguMXuH7L+OrEvnfvTrv2bpbmj5juu/yGWWo/hZ678Kk6WcYlfrv4LgesipiOu/6sBOHGHv678ITkmrM4zsv5z90AwCXu2/6AtQs5Nj7r+c9HIehZzvv5JJagJzgvC/jvOYSHpM8b+UEod0QCzyv0awO8Y0IPO/G7KuxSom9L/CobIr/zz1v8qE3KeDY/a/dS1LcVqX97+XbI3/NNb4vw==",
                    "dtype": "f8"
                  },
                  "yaxis": "y2"
                },
                {
                  "line": {
                    "color": "#008000",
                    "width": 2
                  },
                  "name": "Pitch est",
                  "type": "scatter",
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y2"
                },
                {
                  "line": {
                    "color": "#008000",
                    "dash": "dash",
                    "width": 2
                  },
                  "name": "Pitch true",
                  "type": "scatter",
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",
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",
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                  "yaxis": "y3"
                },
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                  "fill": "tonexty",
                  "fillcolor": "rgba(0,0,255,0.15)",
                  "line": {
                    "color": "#0000ff",
                    "width": 0
                  },
                  "showlegend": false,
                  "type": "scatter",
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y3"
                },
                {
                  "line": {
                    "color": "#0000ff",
                    "width": 2
                  },
                  "name": "Roll est",
                  "type": "scatter",
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",
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                  "yaxis": "y3"
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                {
                  "line": {
                    "color": "#0000ff",
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",
                    "dtype": "f8"
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                  "yaxis": "y3"
                }
              ],
              "layout": {
                "annotations": [
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        "# Create 3-row subplot: one for each of Yaw, Pitch, Roll\n",
        "fig = make_subplots(rows=3, cols=1, shared_xaxes=True, subplot_titles=(\"Yaw (deg)\", \"Pitch (deg)\", \"Roll (deg)\"))\n",
        "color_rgb = {0: (\"rgba(255,0,0,0.15)\", \"#ff0000\"), 1: (\"rgba(0,128,0,0.15)\", \"#008000\"), 2: (\"rgba(0,0,255,0.15)\", \"#0000ff\")}\n",
        "names = {0: \"Yaw\", 1: \"Pitch\", 2: \"Roll\"}\n",
        "for i in range(3):\n",
        "    fill_rgba, line_color = color_rgb[i]; mean = ypr_est[:, i]; std2p = 2.0 * rot_std_deg[:, i]; upper = mean + std2p; lower = mean - std2p; row = i + 1\n",
        "    fig.add_trace(go.Scatter(x=times, y=upper, line=dict(color=line_color, width=0), showlegend=False), row=row, col=1)\n",
        "    fig.add_trace(go.Scatter(x=times, y=lower, fill=\"tonexty\", fillcolor=fill_rgba, line=dict(color=line_color, width=0), showlegend=False), row=row, col=1)\n",
        "    fig.add_trace(go.Scatter(x=times, y=mean, name=f\"{names[i]} est\", line=dict(color=line_color, width=2)), row=row, col=1)\n",
        "    fig.add_trace(go.Scatter(x=times, y=ypr_true[:, i], name=f\"{names[i]} true\", line=dict(color=line_color, dash=\"dash\", width=2)), row=row, col=1)\n",
        "fig.update_layout(height=900, title_text=\"Yaw, Pitch, Roll (deg) with ±2σ bounds\", xaxis3_title=\"Time (s)\", yaxis_title=\"Degrees\", showlegend=True)\n",
        "fig.show()"
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    {
      "cell_type": "markdown",
      "id": "fca1d37b",
      "metadata": {},
      "source": [
        "## Plot Velocity (m/s)\n",
        "\n",
        "Velocity often benefits from frequent updates (e.g., GPS Doppler). Here we only have occasional position updates; expect velocity uncertainty to grow between updates and shrink slightly after them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "9d0e25cc",
      "metadata": {
        "tags": [
          "hide-input"
        ]
      },
      "outputs": [
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                  "yaxis": "y"
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                  "fill": "tonexty",
                  "fillcolor": "rgba(255,0,0,0.15)",
                  "line": {
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                    "width": 0
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                  "showlegend": false,
                  "type": "scatter",
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y"
                },
                {
                  "line": {
                    "color": "#ff0000",
                    "width": 2
                  },
                  "name": "vx est",
                  "type": "scatter",
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y"
                },
                {
                  "line": {
                    "color": "#ff0000",
                    "dash": "dash",
                    "width": 2
                  },
                  "name": "vx true",
                  "type": "scatter",
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",
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                  "yaxis": "y"
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                    "width": 0
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                  "showlegend": false,
                  "type": "scatter",
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                  "yaxis": "y2"
                },
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                  "fill": "tonexty",
                  "fillcolor": "rgba(0,128,0,0.15)",
                  "line": {
                    "color": "#008000",
                    "width": 0
                  },
                  "showlegend": false,
                  "type": "scatter",
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y2"
                },
                {
                  "line": {
                    "color": "#008000",
                    "width": 2
                  },
                  "name": "vy est",
                  "type": "scatter",
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y2"
                },
                {
                  "line": {
                    "color": "#008000",
                    "dash": "dash",
                    "width": 2
                  },
                  "name": "vy true",
                  "type": "scatter",
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",
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                  "yaxis": "y2"
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                    "width": 0
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                  "showlegend": false,
                  "type": "scatter",
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                  "yaxis": "y3"
                },
                {
                  "fill": "tonexty",
                  "fillcolor": "rgba(0,0,255,0.15)",
                  "line": {
                    "color": "#0000ff",
                    "width": 0
                  },
                  "showlegend": false,
                  "type": "scatter",
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                    "dtype": "f8"
                  },
                  "yaxis": "y3"
                },
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",
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                  "yaxis": "y3"
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y3"
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        "# 3 subplots for vx, vy, vz\n",
        "fig = make_subplots(rows=3, cols=1, shared_xaxes=True, subplot_titles=(\"Velocity X (m/s)\", \"Velocity Y (m/s)\", \"Velocity Z (m/s)\"))\n",
        "color_rgb = {0: (\"rgba(255,0,0,0.15)\", \"#ff0000\"), 1: (\"rgba(0,128,0,0.15)\", \"#008000\"), 2: (\"rgba(0,0,255,0.15)\", \"#0000ff\")}\n",
        "for i, comp in enumerate([\"x\", \"y\", \"z\"]):\n",
        "    fill_rgba, line_color = color_rgb[i]; mean = vel_est[:, i]; std2p = 2.0 * vel_std[:, i]; upper = mean + std2p; lower = mean - std2p; row = i + 1\n",
        "    fig.add_trace(go.Scatter(x=times, y=upper, line=dict(color=line_color, width=0), showlegend=False), row=row, col=1)\n",
        "    fig.add_trace(go.Scatter(x=times, y=lower, fill=\"tonexty\", fillcolor=fill_rgba, line=dict(color=line_color, width=0), showlegend=False), row=row, col=1)\n",
        "    fig.add_trace(go.Scatter(x=times, y=mean, name=f\"v{comp} est\", line=dict(color=line_color, width=2)), row=row, col=1)\n",
        "    fig.add_trace(go.Scatter(x=times, y=vel_true[:, i], name=f\"v{comp} true\", line=dict(color=line_color, dash=\"dash\", width=2)), row=row, col=1)\n",
        "fig.update_layout(height=900, title_text=\"Velocity Components in World Frame with ±2σ Bounds\", xaxis3_title=\"Time (s)\", yaxis_title=\"Velocity (m/s)\", showlegend=True)\n",
        "fig.show()"
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    {
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      "source": [
        "## Plot Position (m)\n",
        "\n",
        "Position updates should pull the estimate toward truth and reduce the ±2σ band. If divergence occurs, check:\n",
        "- H matches EKF local coords ([0, 0, R, 0] for world-position).\n",
        "- Measurement noise R reflects your sensor.\n",
        "- Process noise (IMU) is not overconfident."
      ]
    },
    {
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      "execution_count": 11,
      "id": "2ea60e82",
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          "hide-input"
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                  "yaxis": "y"
                },
                {
                  "fill": "tonexty",
                  "fillcolor": "rgba(255,0,0,0.15)",
                  "line": {
                    "color": "#ff0000",
                    "width": 0
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                  "showlegend": false,
                  "type": "scatter",
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y"
                },
                {
                  "line": {
                    "color": "#ff0000",
                    "width": 2
                  },
                  "name": "px est",
                  "type": "scatter",
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y"
                },
                {
                  "line": {
                    "color": "#ff0000",
                    "dash": "dash",
                    "width": 2
                  },
                  "name": "px true",
                  "type": "scatter",
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                  "yaxis": "y"
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                {
                  "line": {
                    "color": "#008000",
                    "width": 0
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                  "showlegend": false,
                  "type": "scatter",
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y2"
                },
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TE8DMPxkgSiETwMKJ4PM+LxPArvjLc0g9E8CQQjgoZksTwMVqyFKXWRPANUSij91nE8DivIBHN3YTwJf515qihBPAn1ipdCCTE8AtWECVsaETwEhd6dlTsBPAX5BEWAW/E8DxuwIcx80TwMRWGd6W3BPAzP1b4XPrE8CAkQb1YfoTwJS4wHheCRTARiT7D2YYFMDC4x3JeycUwF7w61uhNhTA1tyCaM5FFMD+hJbb+lQUwIrv9+omZBTA/iassUtzFMDy+oLxYoIUwPMj6bBukRTAtaT1b3GgFMB46ss9ZK8UwJ2I27FAvhTActfEwQrNFMB1YcbDvtsUwPvOW21Y6hTAjkxROXP6D8DbzsH+MQUQwOXzJoUUDRDA243EHeUUEMDE4L/kqRwQwERmqixkJBDAGv0gxRQsEMABt0howjMQwOYxTQVwOxDA+dwf3B9DEMB/BZU22UoQwNLCwc2kUhDA5YQ5K8FqEMD8N+OwensQwAmoeC6ZlRDAWVBTUZaeEMAHh7+iuqcQwOEbC1ADsRDABsn2FG26EMCuWmta+MMQwECYT5yhzRDA/iIK7GTXEMAa1JHEQeEQwPxjOuM26xDAJBF15D/1EMCG011ZWP8QwPEK5TN+CRHAB6jkGa0TEcDKjLHQ4B0RwNxfwlYXKBHA0gXrPk0yEcBen/oxfzwRwAqzcyqsRhHAsv0MKdRQEcCwDGCq9loRwOqtviMTZRHAKdEHKSpvEcC89Qc7PHkRwDwvzs1JgxHAfIjJ01ONEcAimjyzW5cRwKSDJsBioRHAJ68DOmqrEcBP5iLRc7URwKAwaZyBvxHAsYuN8pXJEcByXw5Ks9MRwEBebebc3RHAhe+oKRboEcCyoSWvYfIRwMMsMOTB/BHAgBNp4DkHEsC8tZRNzBESwN067eN6HBLAY885pkgnEsDQafxFODISwNbOLIBKPRLAr9JXMIBIEsDIj3Sr21MSwObmZJJeXxLAaM0tyghrEsAN1ZFu23YSwDTjaWLXghLAEISthvuOEsB44S+aSJsSwHyMbVe/pxLA3uN4zF20EsC7gs+9IcESwLXHiOALzhLA3qe2dBzbEsDg9BY6UOgSwBryEDyn9RLAKJY1ZCEDE8ANf2iuuhATwDLZBBJvHhPAHBQ8iz8sE8A5Ln5mLDoTwLQ6xbEwSBPA2mq7zUxWE8DTQ9DpgGQTwJxqe8LHchPAqPgAdCGBE8Cgd0GPjY8TwAA4ln4FnhPAQBSf+IKsE8CQrKOJBbsTwM3bTzKMyRPAChH0PBHYE8Bmm26QluYTwNHy57ke9RPAWpzWeqYDFMByxyFvKxIUwMTQg0yxIBTAmsRNOzsvFMAMCWQyxz0UwFgeFSlaTBTA4E6h1ftaFMDG+AWfrGkUwFKW9sNveBTAnHHAVEiHFMDsSpjP9VkQwPgkuWrNYxDAgS5oL7ZtEMDy7IyLsncQwNydeALAgRDA3v0N3eCLEMD1n59YF5YQwGCuaRZhoBDAS8BX7buqEMCwTk8AKrUQwN9R4PetvxDA5CBfzkbKEMDoo56/99QQwIZHs0DE3xDAXkHeSqvqEMCq0fKCrvUQwFH3owPPABHA7Ik7LQoMEcCxCUUAXRcRwDUpN97GIhHAkc9ZGEcuEcAGanIJ2jkRwNJ/zDZ+RRHAipBqOjJREcDbSEdK8lwRwOMzGhC7aBHAZL2wWYt0EcCKgJJQYoARwNNgUN89jBHA1x5s+xyYEcBjRGq1/qMRwFMVOD/hrxHAfLhtgMO7EcDjAzyJpMcRwAnakQyD0xHAYK5yEV7fEcAgDb2xNOsRwO3pAz0G9xHA1ff3fdICEsDu/ZjpmA4SwHiaRAtZGhLAv0jXNBMmEsA2kHzHxzESwKhTNEt2PRLAUHR1oh5JEsDNy01/wVQSwPrgIqFeYBLAKMyLCfZrEsBSSC/4o44SwMiE1mwrmhLAVvRbX7ClEsB1ZgJLM7ESwI9bgti0vBLAypxOzjbIEsDaiAqgutMSwBS3OL5B3xLADjLrvs3qEsB3+xdQYPYSwMKzYTD7ARPADEZMBaANE8DIvcGUUBkTwJeK890NJRPACHDHtdgwE8BSvGhesjwTwNFsO82bSBPAFG0AsJVUE8BscO2AoGATwPhia2m8bBPAxSkNS+l4E8D2avTTJoUTwLTblbN0kRPA4KSEc9KdE8AcSAVtP6oTwL7wBSK7thPATUQOBkXDE8AigddB3M8TwKy46/R/3BPAUNuXbi/pE8BO0bON6vUTwFl26SmxAhTAe2OOfIIPFMAvYecKXhwUwPhAVGlDKRTAtLxM+jE2FMC0m3g4KUMUwLRUkEspUBTAECE+wjJdFMA/6XAQRmoUwJh77q1kdxTAGKxG1o+EFMCUaCicx5EUwIcgBRQMnxTALovD912sFMDB1x/hvbkUwLSI2Z8rxxTAhFby9KfUFMBCpjN8M+IUwGBZLrPN7xTAZM09s3b9FMAo8pCXLgsVwGV0B+iGEBHAMNJgNJsZEcBX8m/SvSIRwDGQCyLvKxHAPTPHii41EcCFpsNMfD4RwI9uypnYRxHAz9d4x0JREcBpCi85uloRwPILvlc/ZBHAqJ4AttJtEcBvFvIPdHcRwC1fRlAkgRHAPsPXPuSKEcBwE4lVs5QRwMLfZR+SnhHAbH4nLYGoEcBr9snmf7IRwD5VSmWNvBHAUy3sZ6nGEcDk9au009ARwGtlmUIL2xHANwTDs0/lEcC6QGCXoO8RwEDh6pb8+RHA0JrbRmIEEsBRX/Dw0A4SwPGEtxlIGRLApBL78sYjEsCS02oOTS4SwG7NElDaOBLAdGJ/oW5DEsDhqCCfCU4SwEY7vA+rWBLAJbAoKVNjEsDK4wAjAm4SwONE7q+3eBLAbL9RfHODEsD/6xi6NY4SwPSCUvL9mBLAdosV8MujEsB0nTg1oK4SwAbQ0RF7uRLAZ1ASq1vEEsD6pTpKQc8SwHj0S9Is2hLAdgv1hh3lEsAaC7PmEvASwAOvb0QO+xLAhvwDjw4GE8AHY0lZExETwPcuGoAeHBPAt97MQjEnE8A+btTrSjITwLCAG1xrPRPAkF/qXJRIE8CFmijFxFMTwDDe9Gf8XhPAInOCVD1qE8BqV6EviHUTwOy6vLjcgBPAKpOp2jqME8CQCXQ7o5cTwIk7EBsUoxPAzpoadYyuE8DeXWG2DboTwFyvdVSVxRPAfme6hCHRE8AwZUgqtNwTwJISsqxO6BPAgMb68O7zE8ANQEHzkv8TwJ7cxEk8CxTA3LB5YegWFMBSZeexlCIUwKVkK9VCLhTAkhrCgvQ5FMA8feP4p0UUwKuHGnpbURTAKCgmCxFdFMCMvrS8xmgUwIhkDdt6dBTAj89dxC+AFMDZQpmT44sUwPDeD+yUlxTAFnbfuUajFMDALa+K+64UwNbxvOuxuhTAfnMPv2jGFMD3g8QAI9IUwO3r2nPf3RTAuhMIKp3pFMD+nRVlX/UUwIIDmDspARXAntPuc/kMFcBUd35wzhgVwHLGcYWqJBXAHEme64swFcA4z5wicTwVwBO0nclcSBXAJV+1zJWiEcD212OcVaoRwGxoE0AbshHAMA75Cum5EcC5amuEvcERwJZeA06XyRHAZO95vnjREcCfqfNVYNkRwMja3ZlM4RHAsOcnvT/pEcCQ1zTVO/ERwDm1Z5M/+RHAzLvU0kkBEsCfkqXPXAkSwAdkD2t3ERLAZHDzlpgZEsAxFFKKwiESwLLlrn/0KRLAJ/LhrC0yEsAXwHArcDoSwO+tkMK9QhLALpcTvBVLEsD8ThGgd1MSwMQcVWPlWxLAFEIC+V5kEsBwsH8h5GwSwNY1epF2dRLASr1iGxh+EsB/rThcyYYSwOfyEt2KjxLACmND+12YEsCJWtWIQ6ESwNk7BYA8qhLAMbB1NUqzEsDX+ew7brwSwE143+ypxRLA3rPntv3OEsBUFekGatgSwAB2Qt7w4RLAHc7Z1ZPrEsAavd3RUvUSwDylJqIv/xLAuljcoCsJE8DqvWecRRMTwEol7mN8HRPAkkViSNEnE8BefkvPRDITwBSqBKrUPBPAZtSTQ4JHE8DWSqAtTlITwAonmMw0XRPAgowoxzdoE8BkrtH5V3MTwNv8Dh6RfhPAD4bEGeCJE8DOqlsWR5UTwGlLOifHoBPAc9IFjVysE8BRFBcUC7gTwM+5WbrVwxPAmLtNerjPE8DEnoQ/sNsTwHI0vaW/5xPARFvgoOfzE8D1zQ6sIwAUwNhekjR2DBTAxrFx3uAYFMBluWAnXyUUwG7/LCf1MRTAHUhkCac+FMCyRE3vcEsUwLYZerFPWBTAmZ2tpUdlFMBopVINXHIUwLNlLNyIfxTAFJthLdKMFMAR62DeO5oUwC3EANfBpxTAgpGDlGC1FMD7VEU/HMMUwCaQxgz40BTAktoX1e/eFMAHxpQWB+0UwHKWGeZA+xTAOUKts5gJFcAeeyR9ERgVwGdOHhmuJhXANB5oZGk1FcD4oUVyPkQVwKai9CgvUxXA8N7o+TxiFcA+jtGRYnEVwBCsMO6ggBXA7EQe7fiPFcBecZY7ZZ8VwBYOEtfgrhXAPgdhC2y+FcCiRujZBs4VwGcdnTqs3RXAMA+PGFztFcB3YxvgfmMSwKR5zjMebxLATtDOh8R6EsDGwsiFcYYSwG/QpYEhkhLAKu6OStGdEsBc29NngKkSwO3LI4wutRLAWESjYtnAEsDBBGm5gMwSwBztLl4k2BLANc4YgMLjEsCUfhkjWe8SwGD458Xn+hLAnlUZBm4GE8B6PkDQ6RETwLLRjxRaHRPAwD+fNL8oE8CFky2xGTQTwJ+bp6RoPxPAK28XWKtKE8CWQ7CV4lUTwPgUQXYPYRPAtNTs1TFsE8AM7NYPSncTwGh1+21aghPA6r5crmKNE8CK/V2nYpgTwE5CMshcoxPAkk145VKuE8CI0c1JRLkTwPvI/nQwxBPAvUOp4BnPE8BOPGhg/9kTwMZmRqbf5BPAq7GJD73vE8Cm/z2LlfoTwCqg9dtnBRTAQfGPOTcQFMBDA4zNBhsUwGdrM/bVJRTAFZYmvKMwFMBnEU/3cjsUwEG7BONBRhTAmvK9Vg5RFMBLspRN2lsUwNSrawmoZhTAh4Q9WnVxFMAqcD/MP3wUwM3iWbQJhxTAvm51rtCRFMASM2dmkpwUwCfwPv1QpxTAJ0mDzQmyFMCrZp9yurwUwID8aehkxxTARrB98QrSFMAL78QjqdwUwJNQeDc85xTA18r5d8bxFMDo+tEdRfwUwJS7uIC1BhXASq7oYRoRFcBCBJgSdxsVwDP2R0fJJRXAh9kl3w4wFcBAe/9yTDoVwBLKLGCBRBXAMICIWK1OFcBsMMMA1lgVwI7vUTf7YhXAOshyGx1tFcCECrZOQXcVwKiEy4FtgRXAvCDIQqKLFcBIzXpg4JUVwBim5UIvoBXAp2t+W5GqFcBgHVduCbUVwHzCNC+gvxXAcoF/slzKFcBCPneWPtUVwNJTA6hE4BXAiNlsYXLrFcAAEgtKxvYVwC1UF2o+AhbAjMhH29wNFsAMlzC2nxkWwA==",
                    "dtype": "f8"
                  },
                  "yaxis": "y2"
                },
                {
                  "line": {
                    "color": "#008000",
                    "width": 2
                  },
                  "name": "py est",
                  "type": "scatter",
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y2"
                },
                {
                  "line": {
                    "color": "#008000",
                    "dash": "dash",
                    "width": 2
                  },
                  "name": "py true",
                  "type": "scatter",
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                  "showlegend": false,
                  "type": "scatter",
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R+D/QOJybcib4P3fR6fbDOvg/qbp5iYFO+D9n3jH4p2H4P2s5ORQ2dPg/2CJyMieG+D8Qrj9le5f4P0dM3uM0qPg/6/a9VlW4+D8LnRMQ4Mf4P20OSZDU1vg/rC95lDTl+D9gGNLSCfP4P8LQP9BTAPk/bL0AiRoN+T/908kBdxn5P/vbHGphJfk/BDNHotEw+T+yG3vW4Dv5P93PRYWnRvk/wMqwEBtR+T+vbcMTL1v5P1XpBRfyZPk/EefQFzVu+T+mI18fzXb5PzBmQnLNfvk/QPHIYEmG+T81l149DI35PxDRdLnjkvk/15egp+yX+T+ntJlu/Jv5P/lmm5frnvk/FJY2x3Fi9D9m0NPoMWD0PwIdtDulXPQ/ITi7BvhX9D8pGEAgWVL0P/avbVGpS/Q/cVGx5spD9D80jJd/9Tr0P430AqUJMfQ/HxTGR+ol9D+ph3ZG2Rn0P/AYaZYfDfQ//FhQ5Erk8z8GnmndfsbzP90cJmaAlfM/ycMKPlKE8z8wa8J1FHPzP9xEXxWuYfM/9YX9cQVQ8z+GYzxDZD7zP9+joQyvLPM/tMxt0ssa8z8Vn8J2AAnzP5WK9XaM9/I/7ptuhk/m8j9cWF4EKNXyPyy5lvlSxPI/2AFz8q2z8j9XmNQXF6PyPwYiayDLkvI/GF2i86aC8j/BCAfxiHLyPxx4t1qqYvI/lHF5JEBT8j8kypI7IUTyP+zsEf0lNfI/BmkQ0oQm8j/IAI0UFxjyP7vWFGC3CfI/0Q/mHJ378T8UcEy6/e3xP0iXrk+24PE/XAWnG6bT8T+0T+CpA8fxP8pIGZiuuvE/hdQNlYeu8T/MZlG/w6LxP76cgEJEl/E/5ju2BuqL8T8LImkS6IDxPzbnfyludvE/X90PbWBs8T/AZN+HpGLxP43t5RhoWfE/BkqpipNQ8T94BuFzDkjxPy6fI54DQPE/O/oTHpo48T9kvE2NujHxP1aNJy1QK/E/kqubmH4l8T+SpBsuMSDxPzL9aVpTG/E/1gzVBQUX8T/hxPF/NRPxPyqaMyXRD/E/ALsYSPMM8T/8XT5dswrxP34pDT4BCfE/tNdKQM4H8T88BhUNLwfxP3paoF4ZB/E/MP0Rp4AH8T/kMXgkdQjxP+bNyusECvE/DiONDikM8T/6nkOT2w7xP/pgbOElEvE/lRmEGwgW8T+hp8hggBrxP/RFgfuSH/E/M0x3NkUl8T84pFZ+lyvxP6WBn5WGMvE/wma4Gg468T8Alog+NELxP5gph9L8SvE/lNoZKV5U8T8eX4zwZF7xP9wXEJYbafE/tEqJ8nR08T9JqLT4YoDxP5zBZLzwjPE/E9FWrCWa8T/MFxl66qfxP+t+kEtFtvE/aZU+eSnF8T8D8n+yadTxP5C74qYY5PE/DYXeLUr08T9wz/AdvGnqP+4MbIHOhuo/ELdIrr+k6j+0DFSLxMPqP0UOVFa04+o/A5R7H9EE6z+4l8pwVCfrP9v9EJ4GS+s/SsiBw7Fv6z8Kz8nxjJXrP7yANGjAvOs/dis9Xvnk6z9S/gnsXA7sP3yxHMwMOew/M9KIlrhk7D9ksSMzoJHsP9xZja//v+w/+IA3C4vv7D9MzXwj+x/tP9Lrpp+NUe0/I5AA2nmE7T/AXYQacLjtP7T6EKaq7e0/smzeh10k7j8ugTsjL1zuP9zISabJlO4/SRjQNFvO7j98hpo1DAnvPwwQ2X9+RO8//QzmQNqA7z/KZ3MeQr7vP77wcspN/O8/oJjOF4wd8D++y5CLWj3wPxfsKP1YXfA/rHzYzlB98D+/Luc4Sp3wP6XCj6NMvfA/1BFySCPd8D+UXUuQ1vzwP7wvd0RtHPE/O0RWCLU78T/8inzQflrxP/FobhfRePE/JMjDZrOW8T9iMeAb+bPxP1dc0n6q0PE/lSqdndDs8T8FwbSwLz3yP4/1j/6NVvI/98TkcQ5v8j/jrSYYv4byP9CwbZqunfI/dlA9zMKz8j92SQjADMnyPxat736c3fI/AHUL+Frx8j/1I8HRNQTzP5rD9xhAFvM/BsirSo0n8z8AMGl8DjjzP/jZhrjXR/M/7q5Djv1W8z8N74g1dWXzP55ebblVc/M//bS9wLWA8z9AE+4LjY3zP9aCBRLWmfM/eAzP6aal8z+UZF3ZFLHzP2Xck8wavPM/FEgOysvG8z/fJ4ATOtHzP8sjBWFg2/M/loFqSDvl8z8GwMxj2+7zP5ztaNJP+PM/HIEfTZQB9D+peVAHtgr0P0sGqezAE/Q/EylZlq8c9D8BlRFyjCX0P3TCSeVgLvQ/TZ0MHiY39D8ad6ma1j/0P8HvLZt6SPQ/4u3zaxpR9D/TtR2ds1n0P3P+v8ZRYvQ/nPqovgBr9D/zlMdHwHP0P9k8ojSQfPQ/xox7XnqF9D9+3kcQh470P6f7+7Czl/Q/UZ+5+gah9D++pYOPhqr0PwzMQNwttPQ/MqDB3QG+9D/uPsIUB8j0P3GAPtQWvfA/Co0S4b3F8D9fJjFKj87wP3x2Aw6N1/A/3MwcpbDg8D8FSq8M++nwP2N3O7Vs8/A/iCjbL/788D84k8JNqAbxP/l4OfNqEPE/aF1ZTkUa8T8FVVYOLyTxP0VWMNgmLvE/nfAckis48T+lO17qNULxP1cXTrNFTPE/8kvFDltW8T/PRQrVb2DxP3EooEd+avE/rNIeeIZ08T9aajSEiH7xPxndiEx/iPE/qogaR2uS8T/6Q7DgTJzxP2lMWcofpvE/I0IgguCv8T+xMjWYj7nxPzuA/H4tw/E/Ivcx3bbM8T+16+ZvK9bxP+TJuaOK3/E//8ia9tDo8T/Ni8Gh/fHxP+b+9dQP+/E/CJyC0AQE8j8HU7B52gzyP+skA7GPFfI/5nUFfiMe8j+rHyiDlCbyP3sMPfnhLvI/Fhovmws38j+93tk+Ej/yP7ywKpX3RvI/5Fsd8LpO8j881rRyXFbyP/lM46PeXfI/sUdkU0Bl8j+tvHF6gWzyP3YNflOmc/I/itgA2Kx68j9X70eylIHyP8EPPv9kiPI/0RCF5yKP8j9n02PozZXyPwVQFqlnnPI/LyduDPai8j821PpUdKnyPwQ0zj3ir/I/kLI+6ke28j/n1ZD+p7zyP2wk9zD/wvI/JIMzakzJ8j+Gfegsk8/yP9C0mu3J1fI/YTVxnOrb8j8O4pUV/OHyP2jQxmfy5/I/b0ZdSsPt8j8bdy5md/PyP2eae/AU+fI/qqQKA5D+8j8Dp0Hw3gPzP/Nse8wKCfM/LsKq8AsO8z99+W+A3BLzP7qg+zmJF/M/Hol49hwc8z+GjBsJkSDzP5h10azgJPM/HVnXmxcp8z+oWr45MS3zPyMjSiQpMfM/Rni+CAs18z8plRyq0zjzP7phJI5+PPM/hgXGzBVA8z8Jo+ofo0PzP2mWZhYiR/M/wjoooo5K8z9SIqH78k3zP83NDRNMUfM/9IGy0pZU8z9LrcsA3lfzP1Q3ULYrW/M/SI5ckXte8z98InGIyWHzP0tbbtAeZfM/iqqmVndo8z9TX8uQz2vzPxp8vVYxb/M/CpOKb6bd7z90ldmLyOHvP+BBH9EA5u8/ayX/yWLq7z90kNMk5+7vP3hODlWH8+8/kKET8VH47z/edoPvMv3vPzjxrp4LAfA/g6f2WoQD8D9zryuYCAbwP1zUfziQCPA/eBGirBML8D/u1OxkmQ3wP50FQSkaEPA/7KI0048S8D+gwSDIAhXwP+Ji5YBtF/A/XbTpMMsZ8D+qGcu8JBzwPwLbRt6CHvA/H4vtcOIg8D+lTpRTQCPwP2+4HXGlJfA/vU3zYQ8o8D/xvlQVfSrwP5nBHFv4LPA/zIWAR4kv8D+tLdu9LjLwPxknw/XnNPA/9uNZYr038D8EdIBirjrwP2cm5Km5PfA/OV8EsOVA8D+ytZe/MUTwP075oVSdR/A/t11C7y5L8D/wHtc4607wPz31PGvQUvA/n8UBVt1W8D+/ZbkeFVvwP+iryvd3X/A/uKpuWQVk8D/BDQo7vmjwP6ZKXwGhbfA/ezalg6py8D8ymbVa2XfwP5ij8eApffA/j5iSXpeC8D95OAIsH4jwPx44Xgi/jfA/ElmBOnWT8D+lZ/kFP5nwPy5IXGQbn/A/HMAAYAml8D+D1J7OCavwPxmTUWQesfA/3u1BgkW38D/etimcgr3wPzx7qofYw/A/LvZ0f0bK8D9YjM0ly9DwP3KL09Fo1/A/sDPbcSPe8D//vdsU9+TwP8jWlHnn6/A/7l5XT/jy8D9JTFypJfrwPygyYRh4AfE/4HeepvQI8T+InMyIlxDxP5BxyOBcGPE/8niGxUog8T/gqYLfaCjxP5QsATeyMPE/rQzJUC458T/apltt4kHxP6Ca5gHHSvE/MgVRg9RT8T9FuNQID13xP6YJ0dZ8ZvE/mO5qOxVw8T+HyzmC3XnxP9yDDJLag/E/yjdGDQOO8T9FKZwfXZjxP83hzQ3sovE/0+5BDaWt8T+PI9EhfbjxP8PK1U92w/E/1tNT3JLO8T/TjJJnxtnxP09rpZQR5fE/ETHvI3Tw8T8PVmDe4PvxP6mlfBVLB/I/cg9vV7ES8j9z4icREh7yP3IxVYtgKfI/lGCsYZo08j/thZbkq2HuP0chyS1Xde4/VgNkqamI7j9clVbInpvuP4rABAclru4/aQ+7ny/A7j95mj3NvtHuP6XYdALT4u4/alSMNmPz7j+8rUgxagPvP/7fZB3kEu8/I6rgx8oh7z99W4XgGDDvP+UraoTIPe8/D/XfZNZK7z/mt0FyQlfvPzuakJoDY+8/K6YgdBVu7z9jOOc0hHjvP2JVA5tLgu8/DRxRQGuL7z+x43hS/JPvP2GxrvMXnO8/35GSVruj7z9yGXM256rvP8g2G523se8/WlNn0yK47z/bY39uIr7vP96bU27Yw+8/fvgVQmDJ7z+sOcV5pM7vPybcCo2T0+8/nOXxC0fY7z+J9eEfntzvP0UepL574O8/2oBo7v3j7z8fv9C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                  "yaxis": "y3"
                },
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                  "fill": "tonexty",
                  "fillcolor": "rgba(0,0,255,0.15)",
                  "line": {
                    "color": "#0000ff",
                    "width": 0
                  },
                  "showlegend": false,
                  "type": "scatter",
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Y97+YchIGyt73vyts9rWx5fe/ryLsJTXt97/nIKskV/X3v59bX5wY/ve/fgERBH4H+L/yvTTOhhH4v1FK/SswHPi/ay0mNngn+L8dMhBNWzP4vxvco2HZP/i/GFOZe/BM+L/EnQCmllr4v0CSoxHMaPi/OpExjoh3+L83wZ7Ssob4v9nLVmVSlvi//heic2+m+L9MXkcB8bb4v7MJaKe+x/i/Wmd/euPY+L9hcuhibOr4vyHNwD1L/Pi/wXWKLrAO+b+i6F2tyCH5vxDVEZaCNfm/wE6ps8tJ+b9nDTaB3V75vzppskztdPm/Oexw8eCL+b9hGpCG56P5v2O3QpQuvfm/zNXT1e7A879WptwjBtnzv5I0kBd58vO/vwGk7BwN9L+7rdD8wyj0vzBuN4SPRfS/39YUC59j9L/0EMbzu4L0v2H5zYkGo/S/9awU3Z3E9L8dbsMjQOf0v2rNkSmjCvW/3pXJlAd49b8gw2DN98P1v8lPq3uSOva/HcSyDRhj9r/2al9Ts4v2vw4f4Ct9tPa/5+dLtozd9r8870unkwb3v9ec9cCqL/e/jEqywetY979JjeekDIL3v8MIZnLKqve/0iinCkbT979O+qNmnvv3vyCuT6OTI/i/mqYOXUdL+L/F9VX52nL4v3bGH+8Pmvi/4oi69QfB+L89O/Tr5ef4v0x4y2pvDvm/3HWC3W00+b/6D6AJClr5v7yfpFZrf/m/gChq1lqk+b840AIn/8j5v5k+kHJ+7fm/PU9LZaIR+r+kpyvvNjX6v4KGAc1fWPq/itHHxj97+r+GNOGaop36v9xOcNCpv/q/V0ba5XXh+r/Q+BAb1AL7vwK74MbkI/u/BPteDsZE+79fzfL/RmX7v9RvQBk6hfu/E9p6Srqk+7/a3NL44sP7v1NVAviI4vu/XHn6gMQA/L+2ojmCrB78v3Crq8QYPPy/GeK1sORY/L/C9t+qJHX8v9qFKKXvkPy/duUwtSSs/L/c3Ljp18b8v2QnNYAd4fy/sulAYtj6/L+vDg0/GxT9v2TONIr2LP2/8GjwK1JF/b+O2O1VGV39vxo7JxVadP2/xHlC4COL/b+glFCPZKH9v7pnj70mt/2/oJC+OHXM/b98+kdlQuH9vzBTMiSC9f2/0ggXTTgJ/r9qUOyjbBz+v6wXod4XL/6/c30t1DhB/r8bhnoq0VL+v2aY/Y3eY/6/xfS+rVx0/r8IToWPSYT+v4c0al6qk/6/wM5niYSi/r/UeX3f0LD+v+LO5gWMvv6/ONyY/7/L/r/6xHlRX9j+vzgOMYFe5P6/4FCDy8rv/r9ZIfrmsfr+v0SBKP0HBf+/HXe018UO/79gKeO5Ahj/v/vu2Sm4IP+/NZ4TnPEo/79txIld2zD/vxBy2VFjOP+/S5WudHc//7/Cal3P6Nz5v8vgYN+p4Pm/GpvYowLk+b+snFTW2Ob5v7x1P8lA6fm/Oj6FcBnr+b+uWthBRuz5v2Slz9bi7Pm/SVLAygnt+b9DOIPbn+z5v5JaBqSQ6/m/h1wcGQXq+b/DRTqT6uf5v4RbZxcw5fm/zAHRfv3h+b8ENAZuMt75v8oXMvyw2fm/SCuVoJ7U+b+ewqxKHs/5v394iMsQyfm/Cqem0VnC+b+AGlRWILv5v+5LjVJGs/m/9RAi4rCq+b/NTYTBi6H5v3IRmr8AmPm/ejWGi/eN+b9iSnTKWoP5vwb4H4RYePm/Njl/Z9ts+b+nDHb10GD5vzneqZFsVPm/6l06L6BH+b/2KrNgYDr5v0Fw4DrmLPm/LOG+Zmgf+b8ZBs6v3RH5v70iN5Y+BPm/zEEulsD2+L9GaKKJW+n4v/qhQxkI3Pi/0/A1zvnO+L/45LgHYcL4v72b7Ww2tvi/bqae43Oq+L8ItuvfR5/4v7Uo/paqlPi/h3/3mpOK+L9Lx53cP3D4vxPlcNfraPi//bV3bHhi+L87beXL11z4v/Zod5r8V/i/qIvm6QJU+L96dJrc2lD4vwBfYs50Tvi/9pNoW+lM+L9Dxh4+TEz4v3z59DiKTPi/0PQmu5BN+L/q6XeIb0/4v5SEE64SUvi/mr38kmVV+L/jgwRfc1n4v56xHiIlXvi/iyMErGNj+L88V7IyN2n4v64Jz/2kb/i/TIXyfpZ2+L90z7QE9n34v2dG1yrIhfi/kIGCXfmN+L8LC+Xddpb4v/Eh/WBFn/i/ykqDoGeo+L+4WdauzLH4v7qNpMJku/i/tCvbYTPF+L8LLLjoKs/4v8OyeQs/2fi/idoZcXTj+L/rW7/xv+34v2Q4QwwY+Pi/ewEv3YIC+b/Mm8YsBQ35v7n+SJiWF/m/1GqkcS4i+b+FmmOOziz5v8+IbQFrN/m/NGtb9PdB+b8BELUZdUz5v9tBRxziVvm/+AdqVjVh+b8cXmwjZmv5v0dRzrp2dfm/OWHiVmB/+b/azONTHYn5v243UASykvm/XNTGQRmc+b96b2LKTqX5v1eheKK25fS/XHAtkqHs9L9DHaMaZPP0v0rl1Dr8+fS/ON2UdHAA9b8R3cXKvwb1vynQ8c/pDPW/FKTg9PUS9b/+61Zs6xj1v/fBz1fKHvW/Yqr9k5Mk9b91oYWATyr1v63BI4r/L/W/ASXj36Q19b8Re3jqRjv1v+8fLvDlQPW/wHUX6oFG9b8nvf4bIUz1v3nbc13JUfW/FvbSqnpX9b/Uv333NF31v/MBr3L9YvW/5iZYsNNo9b/Qe9NHt271v6+0so2sdPW/Y1CYBbd69b/H3YIN1oD1v3lRoDAJh/W/ZmWPyVON9b9VrnkItpP1v5AhjnYwmvW/ra3dnsag9b/ZgaxHeaf1v+z9JRZJrvW/eNBq2ji19b+pzQa/Srz1v/W0PLN/w/W/SFzZs9jK9b/TnOI3V9L1v4HFq/b72fW/Xs7C/8bh9b/DdCuft+n1v7LiNTPM8fW/EITp5AT69b82c7C2YQL2v4eX8VPgCva/ffB4tYET9r8Hjd3XRRz2vwydH9MoJfa/lv28wSwu9r+D0UKJUTf2v6sX8oiQQPa/QSTnx+RJ9r9xUEpATlP2v7UILKDLXPa/nQAsPVdm9r88gK1+9W/2vyhKVnWmefa/8v2zTmKD9r9HvJJkJo32v5BIDWX1lva/ajkl8tCg9r9qUMuUtar2vxKdyzWstPa/T55Zyrq+9r9caxPM2sj2v8zVLCsY0/a/fVFeCXzd9r+7thEw/uf2v4ErV8aY8va/DltCtFb99r8tWSOPQQj3v2FOgLFQE/e/EvraiIse97/9iG0F9yn3vz6skPCGNfe/tKD+9jBB97/kb/Go+kz3vxYo6GzpWPe/VROxO/Jk979WKzJpGXH3v0NTU05jffe/ckyqIMWJ978nuuKxQpb3v2LDmdXfove/qOf9ZZOv979LBf2EVLz3vy88dV0nyfe/lP1ohxDW978cMJOnBuP3v6X2TygN8Pe/chzbGif9979rt2OCSgr4v1xSJuFtF/i/DKlPSZUk+L8Mn/0zxTH4vw/MKb70Pvi/OvPD9idM+L/FoaKOYln4v/jsnPCaZvi/r8ZWDWtI9L8wzWsjEVT0vyg1xKywX/S/1wi74z9r9L/GrdKJwnb0v/RcSPQ7gvS/8g59zaSN9L85vfceB5n0v5Qxs6RspPS/k01zZdCv9L+3KfBgLbv0vwQ1UheMxvS/rHUcE/TR9L/GZtnFXt30v2NYvZLT6PS/QOvd1Vj09L/+0Icd5v/0vyAqy9eAC/W/o2A5SS4X9b+WgIJp5SL1v8x9F2GdLvW/7VScrVk69b/tpKRvHUb1v5fFx3jfUfW/r1yDE6Jd9b93M6OXZmn1v2Frr+cidfW/fjm9hc6A9b85GBNraoz1v4Erkfr2l/W/tqWuYWuj9b/s4ebcx671vwPNty8NuvW/pcabaTTF9b9K3oHMPdD1v3xEzuUo2/W/2W3MD+/l9b+oYeR2i/D1v8lUmLH++vW/ZfQzhkkF9r83E2TRaA/2v5IDa8hbGfa/6BGQ/iEj9r87+/Iouyz2vzjFk5YpNva/3dZLXm4/9r/AHx8kjEj2vzadUY2HUfa/o8yGKGRa9r9/vz6QJGP2v8xTC4DMa/a//m2QS1509r9zppPO2Xz2v/qeF7pBhfa/PoezSZiN9r8htgiy2ZX2v91tAJEEnva/jBzfLBym9r+QiL4KHK72vz5z9JL+tfa/YAlcysa99r/sM3+fd8X2v2DDxxwKzfa/EvC51HzU9r8LA7Xo1dv2v3i9GacP4/a/KA/9cSXq9r/77vdZHfH2v7A456Hv9/a/gpZZF5L+9r+ATDbpCgX3v3qnRPBfC/e/pL7aeYcR979EdURcehf3v8aHQQRAHfe/I315wtAi979+aySoJCj3v3CWWuVFLfe/kI/QPj4y978TC0mBBTf3v7jfELOXO/e/qtsGJABA97+7pkfHOET3v2om5aM8SPe//Pz3AxhM97/vIwjIxk/3vw1XHOZDU/e/q/L1XJ1W978/Nojh4Fn3v2s8KF8MXfe/GDrPgh9g97/dsZBEKWP3v31CfGIqZve/kz9k1iNp978fK9bUJGz3vz1QYk47b/e/pn2OKmly979xgYgPsXX3v8Ad0C4gefe/riW0L7l8978HxOcVI8rzv45GCY29zPO/k82/Uo3P87+Av+vNlNLzvy1v1mvc1fO/175CJmrZ87/CHdS/Pd3zvw2SQZhW4fO/zdqBlLjl879k+APGZerzvze2HaVf7/O/mOGjb6j0878xv44LQvrzv9nIfPkuAPS/BGDmGHAG9L/dv/aBBA30v4011fnvE/S/l8y/LzQb9L/Ex/yuyiL0v00fgCy1KvS/O44PHvMy9L/F+BYHeDv0vzggyrU3RPS/sfo2DjNN9L9zzIkAalb0v1LYoSTPX/S/Tc21Rmdp9L+gMrH3NHP0v7WoR/YnffS/z+wxBzOH9L+Aln06X5H0v7MjIc61m/S/ypyLwCqm9L/YlYi/zbD0v9CQv52su/S/C1UyULnG9L8888KPCdL0v2/BZUiw3fS/l/s8dJ7p9L+uBgX+xvX0v54JZQo8AvW/xBpZxA4P9b9sXP3MLxz1v5r1TmSxKfW/+sCUmKM39b8AB/jZ9EX1v87O4kmUVPW/IB8AKI9j9b9g7NSV8nL1v6LHjP6qgvW/rhEX9cWS9b9MhmMUT6P1v38w+igwtPW/kZ4qGHbF9b8vjgEyKtf1vyZPWd4z6fW/ynh8Xnz79b9yfJWhDQ72vwH7n//wIPa/wlNAXw409r9NDZgacEf2v8Gw1yYdW/a/RWKvyvhu9r9Y9l9z54L2v2zsAhTtlva/PHmNQwur9r/Q8TraIb/2v/ULgcU00/a/Cq8apkbn9r+oMz1RN/v2v7xEI6gLD/e/CHRH2cUi97+PXNmrRDb3v9F3V7RoSfe/4aMhnzRc979FVgTVq273vwvODOCxgPe/t/H3bk6S97+eW7QAjKP3vzgrbERVtPe/K1dRcZTE97+O5G2QUNT3v2ZF1Z2Q4/e/YMIDE0Hy97+xP0KubAD4v29sBOoeDvi/NBpUPEkb+L82dicA+if4vw==",
                    "dtype": "f8"
                  },
                  "yaxis": "y3"
                },
                {
                  "line": {
                    "color": "#0000ff",
                    "width": 2
                  },
                  "name": "pz est",
                  "type": "scatter",
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",
                    "dtype": "f8"
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                  "yaxis": "y3"
                },
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",
                    "dtype": "f8"
                  },
                  "yaxis": "y3"
                }
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      "source": [
        "# 3 subplots for px, py, pz\n",
        "fig = make_subplots(rows=3, cols=1, shared_xaxes=True, subplot_titles=(\"Position X (m)\", \"Position Y (m)\", \"Position Z (m)\"))\n",
        "color_rgb = {0: (\"rgba(255,0,0,0.15)\", \"#ff0000\"), 1: (\"rgba(0,128,0,0.15)\", \"#008000\"), 2: (\"rgba(0,0,255,0.15)\", \"#0000ff\")}\n",
        "for i, comp in enumerate([\"x\", \"y\", \"z\"]):\n",
        "    fill_rgba, line_color = color_rgb[i]; mean = pos_est[:, i]; std2p = 2.0 * pos_std[:, i]; upper = mean + std2p; lower = mean - std2p; row = i + 1\n",
        "    fig.add_trace(go.Scatter(x=times, y=upper, line=dict(color=line_color, width=0), showlegend=False), row=row, col=1)\n",
        "    fig.add_trace(go.Scatter(x=times, y=lower, fill=\"tonexty\", fillcolor=fill_rgba, line=dict(color=line_color, width=0), showlegend=False), row=row, col=1)\n",
        "    fig.add_trace(go.Scatter(x=times, y=mean, name=f\"p{comp} est\", line=dict(color=line_color, width=2)), row=row, col=1)\n",
        "    fig.add_trace(go.Scatter(x=times, y=pos_true[:, i], name=f\"p{comp} true\", line=dict(color=line_color, dash=\"dash\", width=2)), row=row, col=1)\n",
        "fig.update_layout(height=900, title_text=\"Position Components with ±2σ Bounds\", xaxis3_title=\"Time (s)\", yaxis_title=\"Position (m)\", showlegend=True)\n",
        "fig.show()"
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    {
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      "source": [
        "## Conclusion\n",
        "\n",
        "This notebook demonstrated the use of GTSAM’s Gal3ImuEKF for fusing IMU and position measurements in an autonomous surface vessel scenario. We visualized the EKF’s estimates and uncertainty for orientation, velocity, and position, comparing them to ground truth. The results show that the EKF tracks the true state well, with uncertainty growing between updates and shrinking after position corrections. Careful tuning of process and measurement noise is essential for reliable performance. This workflow provides a foundation for integrating additional sensors and handling real-world data imperfections."
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